Healthcare technology is a rapidly advancing, interdisciplinary field and Healthcare Technology Letters is designed to meet the needs of researchers working in this context by providing a home for reporting on the diverse technologies and approaches being brought to bear on healthcare. Now a fully Open Access journal, Healthcare Technology Letters focuses on the most recent advances within a wide scope of disciplines, encompassing biomedical engineering, and computer and information science for healthcare.

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% () and 99.31% (), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

This Letter reports on the testing and assessment of an optical computed tomography-navigation system for percutaneous lung interventional, SIRIO, showing how the lesion diameter affects the bioptic procedure. Clinical data, relating to 501 patients, were collected at the Department of Interventional Radiology of Policlinico Universitario Campus Bio-Medico. This Letter shows that the diameter of lesion affects only the procedure duration (50.91 ± 18.87 min for lesions of diameter ≤20 mm and 44.98 ± 19.43 min for lesions of diameter >20 and ≤40 mm). For the nodules with a diameter ≤20 mm, there is a significant increase in the duration of the procedure (for each mm less the time increases by 6 s). Other parameters like the mean effective radiation dose and the presence of a diagnostic or non-diagnostic specimen do not depend, instead, on the lesion size. The economic analysis based on the biopsy procedure with SIRIO shows the necessity to adopt a new reimbursement system for percutaneous biopsy performed using navigation systems to stimulate their use to get important non-economic gains such as early diagnosis, reduction of the absorbed dose of X-rays and increasing number of lung cancers in a curable early stage.

Eye-tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used to monitor participants’ eye state for collecting eye-movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost-performance ratio and fast computation time, the fuzzy K-nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real-time applicability of the proposed approach and is capable of detecting driver fatigue.